Quantitative researchers will develop mathematical, statistical, and machine-learning models for systematic trading, working in Python, with C++ and SQL as desirable, alongside large-scale data analysis. The role spans full research-to-production, collaborating with portfolio managers and engineers at a global quantitative investment firm. No team size is stated.

Deloitte★3.9 (14k)· London

You will work with Python, NumPy, Pandas, scikit-learn, LangChain, Hugging Face, LlamaIndex, Azure Machine Learning, Foundry, Git, and Agile practices. The team is Deloitte’s Tax Digital Innovation AI & Data Studio, which researches, builds, and delivers analytics and AI-driven solutions for tax and legal clients.

Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

Develops enterprise data platforms using Python, SQL, and Databricks, building ETL/ELT pipelines and working with data lakes and lakehouse architectures.

Use Azure, Databricks, Python, PySpark, SQL and Azure Data Factory to build scalable ETL/ELT pipelines and cloud data solutions. You will design and maintain data systems for a London Market insurer’s enterprise platform, supporting smarter decisions across Underwriting, Finance and Operations.

Work with Python, SQL, SHAP, Airflow, PyFlink, BentoML, Git, Jenkins, Octopus, Docker, JIRA and MLFlow. As a Data Scientist at Zempler Bank, you develop algorithms to protect customers, widen credit access and improve financial outcomes. You implement and monitor models in production.

You will work with Python, NumPy, SciPy, simulation modelling, optimisation under uncertainty, cloud deployment, and machine learning. The team builds an AI operating system for secure environments, taking complex scientific models from prototype to production across government and commercial programmes.

SQL and Python are used to develop pricing strategies, analyse marketing and conversion funnel performance, and evaluate product expansion opportunities. The role supports a fintech unicorn that revolutionises the credit and savings landscape. Mentoring and coaching junior analysts is also part of the position, though the team’s size is unspecified.

You’ll work with QGIS, ArcGIS, GeoPandas, Rasterio, GDAL, Python, and AI-assisted development tools. As part of the ANALYTIQ team, you’ll build geospatial models, reusable scripts, data pipelines, and interactive web tools to accelerate Systemiq’s sustainability projects across energy, materials, nature, and finance.

You will use Python, SQL, and Streamlit on an AWS data platform to clean survey data, build statistical and machine learning models (including NLP), and design interactive dashboards. As a Data Scientist II on the Data Science Consulting team, you will deliver predictive analytics solutions that optimise clients’ marketing effectiveness. You will also manage client relationships and guide more junior team members.

You will work with Python, SQL, AzureML, and XGBoost. You’ll join a small, fast-paced pricing team of 6 at a large insurance company, building alternative pricing models, geospatial models, and residual models from large, messy datasets to improve pricing and customer accessibility.

Use Python, SQL, and production ML pipelines for personalization and recommendations (embeddings, ranking, collaborative filtering) on Muzz, the world's largest Muslim dating and marriage app. You'll also use AI tools, conduct A/B experiments, and work with GCP (BigQuery/VertexAI). The role includes mentoring analysts and data scientists and shaping technical standards.

You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

You'll work with Microsoft Fabric, PySpark, Python, T-SQL, DAX, Infrastructure as Code, Git, and Azure DevOps. You'll design, build, and maintain data pipelines, lakehouses, and data warehouses within Fabric to drive business intelligence and analytics.

Uses SQL, Python or scripting, BI tools like PowerBI/Looker/Tableau, spreadsheets, and Git to investigate fraud and build dashboards. The role sits in fraud analytics at Moniepoint, an all-in-one financial services platform powering POS transactions and banking across Nigeria and emerging markets.

Relay Technologies★2.0 (9)· London

At Relay Technologies you will build and own time-series forecasting models for parcel volume, inbound international volume, and parcel size/weight, using Python and ML frameworks. You will work on the Network squad’s demand-forecasting engine that predicts parcel movement across sortation, middle-mile, and last-mile operations, directly affecting cost per parcel. This is a hands-on individual-contributor role, serving as the technical anchor for Demand Forecasting, collaborating with a Senior Data Scientist, ML Engineer, and Analyst.

Work with Python, SQL, graph databases, graph ML, Docker, Kubernetes, and LLMs. Own and evolve Kharon's risk propagation engine that analyzes entities in a knowledge graph to surface risk signals for global security clients.

HarbourVest★3.1 (16)· London

This role involves productionizing Data Science and AI prototypes using Python, containerized or cloud-based delivery, Snowflake, Power BI, Git, and CI/CD pipelines. You will design analytics workflows and support legacy solutions, partnering with global teams to bridge raw data and analytics-ready assets at HarbourVest, a private markets investment firm.

MAREX★3.2 (6)· London

You will work with Power BI, alongside a Data Lakehouse architecture and a central data catalogue, to analyze and validate structured and unstructured risk data. You will act as the bridge between the Risk function and Technology to support data-led risk management, oversight, monitoring and reporting across the firm.

You use Python and supervised machine learning on tabular data to own and build production credit models that decide which small businesses iwocaPay’s B2B Buy Now Pay Later product can fund. You co-own the full credit modelling stack with one other data scientist.

You'll work with Python, SQL, PySpark, and the PyData stack to build machine learning and analytics solutions for McKinsey's clients across industries, translating business challenges into statistical models and deploying them to production. QuantumBlack, AI by McKinsey is a data science and AI consulting firm applying advanced analytics to solve complex client problems. You'll collaborate with Data Scientists, Engineers, Designers, and Product Managers globally while based in London, with exposure to R&D projects and conference presentations.

Work with Microsoft Fabric (Lakehouses, Warehouses, Dataflows, Pipelines), Azure (SQL, Data Lake, Data Factory), SQL, Python, and REST API/SFTP to build ETL/ELT solutions. You will deliver modern data integration solutions for a diverse portfolio of clients within RSM’s Global VAT Team.

Principal Data Engineer role using GCP, Python, Node.js, BigQuery SQL, and Jenkins CI/CD. The team enables Sky’s growth through Data and AI, deepening customer relationships, optimising commercial performance, and enhancing operational efficiency. The role provides technical leadership, managing Solution Architects and setting engineering standards.

Work with Python, Snowflake, Power BI, Git, CI/CD, and cloud deployment to productionize data science and AI prototypes. You will bridge raw data ingestion to analytics-ready assets, building and maintaining analytics workflows and dashboards for a global investment firm.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.